Exploring the composition of lithic assemblages in Mesolithic south-eastern Norway

¹ University of Oslo

^✉ Correspondence: Isak Roalkvam <isak.roalkvam@iakh.uio.no>

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1 Introduction

This study employs multivariate exploratory statistics to analyse lithic assemblages associated with a larger number of Mesolithic sites located in south-eastern Norway. This is done to identify latent patterns and structure in the relationship between the assemblages, with the ultimate aim of identifying behaviourally induced variation in their composition across time. However, the composition of the assemblages can be expected to be determined by a multitude of factors (e.g. Dibble et al., 2017; Rezek et al., 2020), ranging from the impact of natural formation processes, to various and intermixed behavioural aspects such as purpose, duration, frequency and group sizes at visits to the sites. The assemblages are also likely to be impacted by variation in lithic technology, artefact function, use-life and discard patterns, as well as procurement strategies and access to raw materials. Finally, analytic and methodological dimensions relating to survey, excavation and classification practices are also fundamental to how the assemblages are defined. Consequently, the analysis conducted here is done from an exploratory perspective, where all of these factors should be seen as potential contributors to any observed pattern. In an attempt to limit the influence of confounding effects, the material chosen for analysis has a relatively constrained geographical distribution, and stems from relatively recent investigations that have employed comparable methods for excavation and classification within larger unified projects.

Even though each individual assemblage can have been impacted by an virtual infinitude of effects that might skew an archaeological interpretation, this does not preclude the applicability of inductive analyses aimed at revealing overarching structure in the data without imposing overly complex analytical frameworks that attempt to account for these particularities (Bevan, 2015). Structure that can be revealed from considering all of the assemblages in aggregate can constitute a step in an iterative analytical chain that ultimately aims to tease apart the multitude of factors that have shaped the composition of the assemblages, and should be of value to subsequent in-depth studies of any individual site. The most immediate danger of the approach outlined here is rather to be overly naive in the causal significance and cultural importance that is ascribed to any identified pattern. As such, the main aim of this analysis is to compare the results with findings reported in previous literature concerned with the Mesolithic in southern Norway and have the generation of new hypotheses as a possible outcome. To this end, the analysis follows two analytical avenues. The first involves an analysis of the assemblages using the classification of the artefacts done for the original excavation reports, which consequently assumes that these are behaviourally meaningful. The second involves an analysis of the assemblages in light of the whole assemblage behaviour index, which has not seen application in this context before.

1.1 Archaeological context and material

The 54 coastal sites chosen for analysis here have a relatively limited geographical distribution in south-eastern Norway @(fig:dist)A]. The sites were excavated as part of four larger excavation projects that all took place within the last 15 years (Jaksland and Persson, 2014; Melvold and Persson, 2014; Reitan and Persson, 2014; Solheim, 2017; Solheim and Damlien, 2013). The sites included in the analysis consists of all Mesolithic sites excavated in conjunction with the projects that have assemblages holding more than 100 artefacts. The institution responsible for these excavations was the Museum of Cultural History in Oslo. This has led to a considerable overlap in the archaeological personnel involved, and comparable excavation practices across the excavations. Furthermore, with these projects, major efforts were made to standardise how lithic artefacts were to be classified at the museum. As a result, this should reduce the amount of artificial patterning in the data incurred by discrepancies in the employed systems for categorisation (e.g. Clark and Riel-Salvatore, 2006; Dibble et al., 2017). In this setting, for example, bias could potentially follow from the fact that two of the projects have sites with relatively contemporaneous dates (Jaksland, 2014, see also @(fig:dist)B; Solheim and Damlien, 2013). Any project-dependent classification practice could as a consequence lead to an exaggeration of chronological differences between the assemblages. While this is difficult to fully account for, I do believe that the relative contemporaneity of the excavation projects, as well as the overlap in excavation and classification practices should minimise the above-mentioned effects.

Figure 1.1: A) Spatial and B) temporal distribution of the sites chosen for analysis. Radiocarbon age determintations are given as the sum of the posterior density esimates. Solid lines indicate that the site has been dated with reference to relative sea-level change and typological indicators. These follow the original reports.

A defining characteristic of the Norwegian Mesolithic is that a clear majority of the known sites are located in coastal areas (Bjerck, 2010). Furthermore, these coastal sites appear to predominantly have been located on or close to the contemporary shoreline when they were in use (e.g. Åstveit, 2018; Breivik et al., 2018; Solheim et al., 2020). In south-eastern Norway, this pattern is combined with a continuous regression of the shoreline, following from isostatic rebound (Romundset et al., 2018; e.g. Sørensen, 1979). The fairly rapid shoreline displacement means that the sites tend not to have retained their strategic or ecologically beneficial shore-bound location for long periods of time (cf. Perreault, 2019, p. 47). Consequently, the shore-bound settlement, combined with the rapid shoreline displacement has resulted in a relatively high degree of spatial separation of cumulative palimpsests, to follow the terminology of Bailey (2007), while the reconstruction of the trajectory of relative sea-level change allows for a relatively good chronological control of when these accumulation events occurred. In other parts of the world, a higher degree of spatial distribution means that while the physical separation of material can help delineate discrete events, this typically comes at the cost of loosing temporal resolution as any stratigraphic relationship between the events is lost (Bailey, 2007). However, as the rate of isostatic rebound has varied throughout the Mesolithic in the region, and local topography and bathymetry will have impacted how rapidly a site lost its shore-bound location, this effect is not evenly distributed in time and space. In the earliest part of the Mesolithic, the displacement rate within the study area would have been around as much as 8.8 cm/year, falling to around 0.5 cm/year towards the end of the Mesolithic (Sørensen et al., 2014). Thus, while relative sea-level change appears to have reduced the degree of mixing that has occurred in the assemblages, this could vary depending on when and where they were in use, in turn potentially reducing the degree to which their composition can be directly compared — a point that is returned to in the discussion of the results.

The dates associated with the sites are given. Where radiocarbon age determinations are available these have been calibrated using the IntCal20 curve and subjected to Bayesian modelling using OxCal v4.4.4 (Bronk Ramsey, 2009) through the oxcAAR package (Hinz et al., 2021) for R (R Core Team, 2020). The only constraint imposed for the modelling of the dates was that the dates from each site are assumed to represent a related group of events by means of the Boundary function. The resulting posterior density estimate was then summed for each site.

The lithic data analysed here is based on the classification of the 54 site assemblages done for the original excavation reports, and consists of 48 variables representing different debitage and tool types. While the classification practices for the excavation projects were standardised to an extent, there are some instances where time was allocated to identify additional artefact sub-categories aimed at answering specific research questions. Some categories in the original reports have therefore been combined in the dataset. This for example pertains to the category narrow-blades, which is defined as blades of width between 8 and 12 mm. In the reports, this was only separated from (macro-)blades (width \(\ge\) 12 mm) and micro-blades (width \(\le\) 8 mm) for some of the sites. Narrow-blades were combined with the blade category here. Furthermore, the artefact data have here been divided into flint and non-flint materials. Flint does not occur indigenously in Norway, and is only available locally as nodules that have been transported and deposited by retreating and drifting ice (e.g. Berg-Hansen, 1999). This means that the distribution and quality of flint has been impacted by a diverse set of factors relating to climatic and geographical factors such as, but not limited to, topographic variability, shoreline morphology and ocean currents (Eigeland, 2015, p. 46). Thus, while flint is treated as a unified category here, the variability in quality could have been substantial (Eigeland, 2015, pp. 45–53). Furthermore, the various non-flint raw materials that have been lumped together here have quite disparate properties, where fine-grained cryptocrystalline materials are often used as a substitute or supplement to flint, while other, coarser materials are usually associated with the production of axes and other macro tools. Given this differentiated use, these raw-material properties are expected to be reflected in the retained debitage and tool categories. An important benefit of combining all of the non-flint materials is that this reduces the dependency on whether or not these have been correctly and consistently categorised for the reports (cf. Frivoll, 2017). While certainly a topic deserving of more attention, the general sentiment in the literature is that there would have been stable access to locally available non-flint raw-materials of good quality in south-eastern Norway (Eigeland, 2015, p. 370; e.g. Glørstad, 2011). Finally, while factors such as landscape changes through shoreline displacement still can have led to variable raw-material availability on the analysed sites, the relatively constrained geographical distribution of the sites hopefully counteracts some non-behavioural sources of variation.

Studies concerned with chronological changes in the composition of lithic assemblages in Norway have typically had a focus on formal tool types (e.g. Helskog et al., 1976; Reitan, 2016) or been concerned with technological processes associated with certain sub-categories of the site inventories, such as the production of blades or axes (Berg-Hansen, 2017; e.g. Damlien, 2016; Solheim et al., 2020). Studies that have involved the entire assemblages have either been concerned with general compositional traits such as relative frequency of various tool types and raw-materials (Breivik, 2020; Breivik and Callanan, 2016; Viken, 2018), or invovled extremely in-depth studies of technological organisation associated with a handful of assemblages (Eigeland, 2015; Mansrud and Eymundsson, 2016). These studies are, however, based on narratively driven methods, leaving the weighting of the different variables for the final interpretations unclear. To my knowledege, only a single study dealing with Mesolithic material in southern Norway has involved the use of a multivariate quantitative framework to structure the analysis of eight Middle Mesolithic assemblages (solheim2013a?). In sum then, previous studies have typically either been limited to a small number of sites, to a subset of the inventories, or to more subjectively and narratively driven methods that are difficult to up-scale and consistently balance in the comparison of a larger number of assemblages.

1.2 Methodology

The relatively constrained geographical distribution of the analysed sites, the limited temporal spread over which they where investigated, as well as the methodological equivalency across projects hopefully leads to an exclusion of some biases that might otherwise skew an exploratory analysis, rendering it more likely that behaviourally meaningful patterns are identified. However, the exploratory perspective means that a wide range of combinations and transformations of variables has been explored to identify patterning in the data. While only parts of this process can sensibly be reported on here, the data and employed R programming script is freely available as a research compendium following Marwick et al. (2018), allowing readers to explore and scrutinise the data and the final analytical choices made (Marwick, 2017). However, this inductive data-dredging or pattern searching approach does constitute a limited inferential framework (Clark, 2009), as it involves what Binford termed a post hoc accommodation of explanations to meet the observed data — data that is both selectively and subjectively reported upon. The process can still provide the identification of empirical patterns with respects to the employed units of analysis, which in turn can form the basis for social and behavioural hypotheses. This can lay the foundation for a deductive research agenda with targeted model evaluation for which clear test implications can be derived (Clark, 2009, p. 29).

The first part of the analysis involves employing the method of correspondence analysis (CA), using the lithic count data as classified for the original excavation reports. The purpose of this exercise is to evaluate the degree to which the composition of the assemblages align with patterns that have been suggested by earlier studies — studies that have employed more informally driven methods. This consequently assumes that the artefact categories employed in Norwegian Stone Age archaeology are, at least to a certain extent, behaviourally meaningful. However, the approach taken was also partially informed by the so-called Frison effect (Jelinek, 1976), which pertains to the fact that lithics studied by archaeologists can have had long and complex use-lives in which they took on a multitude of different shapes before they were ultimately discarded. Several scholars have built on this to argue that morphological variation in retouched lithics cannot be assumed to predominantly be the result of the intention of the original knapper to reach some desired end-product, but rather that what is commonly categorised as discrete types of artefacts by archaeologists can instead in large part be related to variable degrees of modification through use and rejuvenation (e.g. Barton, 1991; Dibble, 1995). Consequently, several artefact categories have here been collapsed for the CA. This for example pertains to tool types such as scrapers, burins, drills, knives and otherwise indeterminate artefacts with retouch. That these categories are internally consistent and categorically exclusive in terms of fulfilled purpose is at best a dubious proposition, in turn potentially rendering their contribution as discrete analytic units misleading. These have all been combined into the single category “small flint tools.” (A full overview of the aggregated variables and their constituent parts is provided in the supplementary material). While aggregating artefact categories in this manner could potentially subsume important variation, it does also reduce the possibility that any conclusions are not simply the result of employing erroneous units of analysis. An underlying assumption is therefore effectively that the retained categories represent artefact categories that have fulfilled different purposes or are related to different technological processes. While largely intuitive in nature, it does seem reasonable to assume that for example large non-flint stone tools such as axes, adzes, chisels, clubs and hatches, here categorised as non-flint macro tools, have fulfilled different purposes than the previously mentioned small flint tools.

However, for the most part we lack even a most basic understanding of what any individual lithic object in an assemblage has been used for (Dibble et al., 2017). For example, a vast amount of artefacts defined as debitage are likely to have fulfilled the function of tools, and both debitage and formal tool types could have had various different purposes and had a multitude of shapes throughout their use-life. While use-wear analysis could potentially offer a way to identify what artefacts were used for towards the end of their use-life, these kinds of analyses are extremely time-consuming and are therefore typically only conducted on a smaller number of artefacts that have already been selected for analysis based on their shape (e.g. Solheim et al., 2018). Thus, while these analyses can potentially get at in-group variation pertaining to the end-state of a group of artefacts, they do not tell us whether or not their classification as a unified group is meaningful in the first place (Dibble et al., 2017). This has major implications that the above-outlined analysis does not take properly into account, rendering it difficult to align any identified pattern with specific behavioural dimensions. As a consequence, the second part of the analysis employs a suite of measures developed for the classification of lithic assemblages with these inferential limitations in mind (see Barton et al., 2011; Clark and Barton, 2017, and below). The logic behind these measures are founded on an understanding of technology as being organised along a continuum ranging between curated and expedient (Binford, 1979, 1973). An expedient technological organisation pertains to the situational production of tools to meet immediate needs, with little investment of time and resources in modification and rejuvination, resulting in high rates of tool replacement. Curated technological organisation, on the other hand, has been defined as related to manufacture and maintenance of tools in anticipation of future use, the transport of these artefacts between places of use, and the modification and rejuvenation of artefacts for different and changing situations (Binford, 1979).

However, following not least from the ambiguous definition first put forward by Binford (1973), the theoretical definition of curation, its archaeological correlates, and behavioural implications have been widely discussed, and no single definition has ever been reached (e.g. Bamforth, 1986; Shott, 1996; Surovell, 2009, pp. 9–13). The continuum between curated and expedient technology has for example been related to dimensions such as land-use and mobility strategies, as well as raw-material quality and availability (Andrefsky, 1994; Clark and Barton, 2017; Kuhn, 1992; e.g. Parry and Kelly, 1987; Smith, 2015). Still, that the distinction can offer a useful analytical point of departure if clearly and explicitly operationalised seems more or less agreed upon, and some dimensions of the concept are generally accepted. For example, although precisely how it is measured may vary, the empirical correspondent to a curated technological organisation is typically defined by high degrees of retouch, as this is commonly seen as a means of realising the potential utility of a tool — or extending its use-life — by the repeated rejuvenation and modification of edges (e.g. Bamforth, 1986; Dibble, 1995; Shott and Sillitoe, 2005). Furthermore, one concrete operationalisation of the terms have been forwarded by Barton et al. (1999) and collegues (e.g. Barton et al., 2011; Barton and Riel-Salvatore, 2014; Clark and Barton, 2017), who through a series of studies have shown that the relationship between volumetric density of lithics and relative frequency of retouched artefacts in lithic assemblages provide consistent results across a wide range of chronological and cultural context, and is a measure taken to reflect degree of curation. This is in turn mainly to follow from the accumulated nature of land-use, procurement and mobility patterns associated with the sites (Barton and Riel-Salvatore, 2014). In this model, higher degree of mobility would mean a higher dependency on the artefacts and the material people could bring with them, and dimensions such as weight, reliability, repairability, and the degree to which artefacts could be manipulated to fulfil a wide range of tasks are therefore assumed to have been factors of concern. From this it follows that the empirical expectation for short-term camps is a curated technological organisation with higher relative frequency of retouched artefacts, and a lower overall density of lithics (Clark and Barton, 2017). More time spent in a single location, on the other hand, is assumed to lead to better control of raw-material availability and to allow for its accumulation. This should in turn lead to an expedient technological organisation with reduced necessity for the conservation of lithics and extensive use of retouch. The empirical expectation for lower degree of mobility is therefore relatively high density of lithics, a low relative frequency of retouched artefacts, as well as a higher number of cores and unretouched flakes and blades (Clark and Barton, 2017). These are the main measure employed here. However, as these measures are argued to predominantly be determined by mobility patterns, the investigation of Bicho and Cascalheira (2020) has also led to the inclusion of relative frequency of chips in the analysis as well, as they argue that this is an important. The analysed variables are therefore relative frequency of secondarily worked lithics (RFSL), defined as the number of retouched and ground lithics divided by the assemblage total; volumetric density of lithics (VDL), defined as number of artefacts per m³; as well as relative frequency of chips, defined as artefacts with size < 0.1 cm; relative frequency of cores which are simply all artefacts classified as cores in the original reports, as well as relative frequency blanks, here constituted by the categories flakes, blades, micro-blades and fragments. The analysis is done using principal components analysis (PCA), following the continuous operationalisations of these measures (cf. Baxter, 1994, p. 100).

A note should also be made on the fact that a couple of variables that are sometimes invoked for the classification of sites in terms of associated mobility patterns are omitted here. This pertains to number of site features such as fireplaces, cooking pits and dwelling structures. Feature count has been omitted as taphonomic loss is likely to have led to a chronological bias in their preservation. Similarly, the number of activity areas, effectively number artefact clusters, however defined, has also been disregarded. This follows most notably from the fact that the impact of post-depositional processes at Stone Age sites in Norway is arguably understudied. This pertains for example to the impact of bioturbation in the form of three-throws, which can can have a detrimental effect on the original distribution of artefacts, and which can be expected to have been relatively frequent on several of the sites treated here (Jørgensen, 2017; darmark2018e?).

Here retouch i At logistical basecamps, the extended time spent in a single location would imply a better control of raw material availability, and allow for longer trips to retrieve these. Higher availability of lithic raw materials should in turn reduce the necessity for extensive retouch, meaning the material expectation for logistical basecamps would be relatively high density of lithics and a low relative frequency of retouched artefacts. From this it follows that the empirical expectation for short-term camps is a higher relative frequency of retouched artefacts, and a lower overall density of lithics (Clark and Barton, 2017).

Higher frequency of secondarily worked lithics

2 Results

Figure 2.1 displays the CA using the lithic count data. The general impression from the plots is that a chronological dimension is associated with the patterning in the data. This is indicated by the general transition across the colour scale in the row plot (Figure 2.1A), the clear horse-shoe shaped structure in the column plot (e.g. Lockyear, 2000), combined with the fact that the two first dimensions of the CA accounts for as much as 80.53 % of the inertia or variance in the data. The earliest sites tend to be located in the upper right corner of plot A, with increasingly younger sites towards the bottom along the second dimension. Although fewer in number, the sites from the later parts of the Mesolithic are drawn out along the first dimension of the plot, and are not as impacted by the second dimension as the more numerous older sites.

The column plot (Figure 2.1B) reveals that the earliest sites are characterised by the flint artefact categories microburins, projectiles, as well as flint macro tools and associated debitage. It is also interesting that these sites to a larger extent are characterised by core fragments, both in flint and non-flint materials, rather than the cores themselves. The non-flint material on the earliest, or among the earliest sites, appears to be centred around the production of projectiles, as both the projectiles themselves and non-flint blades are important constituents of the assemblages at these sites. Site number 9, Nedre Hobekk 2, located in the upper right quadrant of the row plot represents a somewhat curious case in that its assemblage is dominated by axe production in metarhyolite (eigeland2014?) . The use of metarhyolite and smoky quartz for the production of axes is present at other contemporary sites as well (Jaksland and Fossum, 2014), but is evidently not as prominent a part of these assemblages. In sum, the findings for the earliest sites are in large part in line with previous research (e.g. Bang-Andersen, 2003; Damlien and Solheim, 2018), although perhaps highlighting the relative importance of non-flint materials for some of the sites in south-eastern Norway which, while also present, is typically not as marked in other parts of southern Norway (Breivik et al., 2018, p. 184).

The first dimension, which is pulling some of the later sites towards the right of the plot, is mainly defined by macro tools and associated debitage in non-flint materials that are negatively correlated with more flint dominated assemblages. Sites with high values on the first dimensions are later Mesolithic sites associated with axe production in non-flint materials, but the later sites occur along the entire dimension, indicating that while these axe production sites are a feature of the later Mesolithic, there is marked variation among these sites. Although the sample size is quite strained and the discussion of finer chronological points might not be warranted, the first dimension does appear to be of of less importance for the absolute latest sites, as indicated by their location to the left of the plot. This could indicate that specialised axe production sites disappear towards the end of the Mesolithic, a notion that would be in line with previous research Glørstad (2011).

With the exception of projectiles, microburins, macro tools and debitage from their production, which are more characteristic of earlier sites, the flint material is relatively similar across the sites, and most of the variation in the data is accounted for by variation in non-flint material. For the earliest sites this largely pertains to the presence of non-flint projectiles and core fragments, while it for later phases is the non-flint macro tools and associated debitage that is the main driver of the pattern. The comparatively limited impact of the flint material can possibly in part be the result of the aggregation of categories that leads to an suppression of otherwise temporally distinct patterns, and due to technological nuances in the flint material that is temporally contingent but which are not recorded during a regular classification of the material. While the former pertains to the trade-off between robustness and sensitivity, the latter is likely to be true for the non-flint material as well, but does speak to the impact raw-material workability has for the composition of the assemblages.

Figure 2.1: Correspondence analysis using artefact count data. A) Object map, B) Variable map.

Reducing the number of such inferential leaps by aggregating artefact groups could potentially subsume important variation, but it does also reduce the possibility that the conclusions of an analysis is not simply the result of the employed units of analysis, and an overly naive trust in the fact that these units reflect any behaviourally meaningful categories. This realisation has led to a wide range of outside Scandinavian Mesolithic research, meaning a range of empirical measures associated with various mobility patterns are available.

It has, however, been suggested that access to raw-materials can be a more important decider for assemblage composition than mobility patterns , and that variable workability of these materials will impact factors such as the relative frequency of tools to debitage in the assemblages (e.g. Andrefsky, 1994; Manninen and Knutsson, 2014). In addition to attempts at accounting for this analytically (see below), the limited geographical distribution of the sites should alleviate the issue somewhat, as the overall availability of raw materials could be expected to be more comparable through time than if a wider region was under consideration.

Of these the most well-estbalished is the. base don the assumption that, this measure and has been shown to display a meaningful measure across a wide range of temporal, geographical and cultural contexts.

Figure 2.3 displays a principle components analysis using variations of the continuous measures that have been linked to mobility patterns. There is a general temporal transition from older to younger sites from the upper left to the bottom right of the plot. The second dimension is mainly defined by a negative correlation between the VDL and RFSL (Figure (ref?)(fig:var-contrib)). Almost orthogonal to this is the strong negative correlation between relative frequency of chips and blanks. While there is a slight tendency for blanks to be more associated with younger sites, frequency of chips appears to be largely independent of time. However, the almost suspiciously strong negative correlation between chips and blanks can perhaps have an methodological explanation. Seeing as the frequency of non-flint material is positively correlated with blanks and negatively correlated with chips, one explanation to this pattern could be that smaller non-flint pieces are simply more difficult to identify and to separate from naturally fragmented stone during excavation and classification, leading to a over-representation of blanks as compared to chips in assemblages with a high proportion of non-flint material. While this is not necessarily the entire explanation behind the relationship, this does render it difficult to Relative frequency of cores is not especially impactful in the PCA, and appears to be independent the temporal dimension as well. That is not to say that cores may not be indicative or related to mobility patterns, but may require further analysis beyond their simple classification as such (Kitchel et al., 2021).

Thus, while some secondary expectations of the Clark and Barton (2017) model does not seem to apply to the present material, it is difficult to say to what degree this is caused by idiosyncrasies in the Norwegian system for classification of lithics and properties of the lithic material itself. The relationship between VDL and RFSL does correspond to the model and follows a clear temporal trend that is also correlated with the increased use of local raw material. Thus, if the relationship between VDL and RFSL is accepted as a proxy for curation, and is related to land-use and mobility patterns, these findings would be in line with previous research into the Mesolithic of Norway, indicating that earlier sites are associated with higher degree of mobility than sites from later phases. To explore this proposition further, these two variables are subjected to more detailed scrutiny below.

Figure 2.2: Correlation plot showing transformation of skewed variables for the PCA, and the linear correlation between these. The mean age of the sites has also been included to visualise overall temporal trends.

Figure 2.3: PCA using variables that have been related to mobility patterns. Note that details on the transformation of the variables has been left out of the plot for clarity.

Figure 2.4: Contribution of variables to the components of the PCA.

Figure 2.5A illustrates the negative correlation between the two variables (r = -0.5) while also displaying a general tendency for younger sites to be associated with a higher volumetric density of lithics and a lower relative frequency of secondarily worked lithics than older sites. The linear correlation is stronger between the mean site age and RFSL (r = -0.51), than between mean site age and VDL (r = 0.22). As variable non-flint availability and workability has also been suggested to potentially impact these measures (Manninen and Knutsson, 2014), Figure 2.5B displays the same relationship, but exclusively for the flint data. While the negative correlation is slightly less marked when only the flint data is considered (r = -0.4), the general pattern is the same. The relationship between mean site age and RFSF is even stronger (r = -0.57), but as indicated by the more spread out distribution along the y-axis, the VDF is not temporally contingent (r = 0.1). As was also indicated by the CA, this follows from the fact that non-flint materials make up a higher share of the assemblages for some of the later Mesolithic sites, and is a point returned to below where the temporal dimension of the relationship between VDL and RFSL is explored further.

Figure 2.5: Relative frequency of secondarily worked lithics plotted against the volumetric density of artefacts (artefact count / excavated m³) for A) All lithics, B) Flint. The logarithm is taken to base 10 on all axes.

To get more directly at this temporal trend, a curation index based on VDL and RFSL was devised by first performing a min-max normalisation of the two variables, scaling them to take on values between 0 and 1. The values for artefact density was then made negative to reflect its relationship with degree of curation. The mean was then found for each site on these two normalised values. To account for the temporal uncertainty associated with the dating of the sites, a simulation-based approach was also adopted (Baxter and Cool, 2016; e.g. Crema, 2012; Orton et al., 2017). A LOESS curve was fit to the curation index and site age for each simulation run, where the age of each site was drawn as a single year from the date ranges associated with the sites as provided in Figure 1.1. For sites with radiocarbon age determinations the dates were drawn from the associated posterior probability density functions, while ages for sites dated with reference to relative sea-level change and typology were drawn uniformly from the associated date range. This simulation was repeated 1000 times, the results of which is visualised in Figure 2.6. Disregarding the edge-effects at either end of the plot, the general tendency is a relatively high degree of curation among the earlier sites, followed by a marked drop around 8000 BCE. This stabilises around 7500 BCE and remains stable without any major fluctuations for the rest of the Mesolithic. The variation in degree of curation is also markedly higher after 8000 BCE, potentially reflecting variation associated mobility patterns.

Figure 2.6: Temporal variation in the curation index for A) All lithics, and B) Flint. The temporal uncertainty is handled by means of a simulation approach, where the site ages are drawn from their repspective age determination probability density functions given in Figure 1B. A LOESS curve has been fit to the distribution for each of the 1000 simulation runs. Each simulation run is plotted with some transparency. Sites mentioned in the text are given colour.

3 Discussion

The results of the CA does appear to align well with previous research that has been based on more informal treatment of the assemblages as a whole, and indicates that the employed artefact categories are capturing a temporal component. However, as several authors have pointed out in relation to the application of CA for seriation purposes, ‘time is not the only dimension.’ The developments evident here do also most certainly correspond to more pervasive cultural change than a purely typo-chronological development of artefact morphology. Unpicking and aligning the significance of these chronological patterns with any specific behavioural dimensions using the CA results is, however, another task entirely. This follows most clearly from the fact that for the most part we do not know what individual lithic objects in the assemblages has been used for, leaving the behavioural and social significance of the employed units of analysis unclear. The results of the CA can, however, be used in conjunction with the part of the analysis that has attempted to get at more specific behavioural dimensions to nuance or explain discrepancies in this data.

While the relevance of the relationship between frequency of secondarily worked lithics and density of lithics was here identified by means of an exploratory approach, some inferential merit can be achieved by invoking what has been termed consilience. Consilience involves “the interlocking or coherence of causal explanations across multiple problem domains” through a clear operationalisation of explanatory terms and concepts (Clark, 2009, p. 30). Thus, while no firm , the clear overlap in results presented here and those repeatedly reported by does speak to the general applicability of the measures, and offers an initial indication that they are capturing the social dimensions of interest also in a Norwegian Mesolithic setting. While it is not within the scope of this paper to consider these comparisons to any wider extent, the VDL for all assemblages treated here ranges from 1004 lithics per m^3, down to 17 which appears to be relatively high when compared to Late Pleistocene sites from western Eurasia. Similarly, the frequency of retouched artefacts ranges from 0.4–11.2 % which is relatively low when compared to the same data. This could, in sum, indicate that the coastal Mesolithic sites of South-Eastern Norway have generally been associated with a low degree of mobility when compared to these context, a proposition that would be in line with both ethnographic and archaeological research that highlights the possible role marine resources can play.

The curation index has relatively high values until some time before 8000 BCE, before it drops and stabilises from around 7500 BCE for the rest of the Mesolithic. This pattern is evident in both the flint data and when all lithics are treated in aggregate. Furthermore, the variation in degree of curation when all lithics are considered could indicate that the sites were associated with a more varied mobility pattern after around 8000 BCE. The five sites that have values on the curation index below c. -0.25 could in this perspective have predominantly functioned as base camps within a logistic settlement pattern. That these assemblages reflect stays of a longer duration was suggested for all five sites in the original reports (Carrasco et al., 2014; Eigeland and Fossum, 2017; Persson, 2014; Solheim and Olsen, 2013; eigeland2014b?), with the exception of for Vallermyrene 4, which was argued to be a specialised axe production site, not necessarily associated with lower degrees of mobility. This highlights a possible issue pertaining to raw-material variability, as the coarse non-flint material used for the production of axes generally results in a relatively large amount of waste per produced tool, possibly skewing the curation index when compared to assemblages dominated by flint. Referring back to the CA, the difference is most marked for the sites in the later part of the Mesolithic where non-flint material become more dominating parts of the assemblages. As can be seen in Figure 2.6B, the degree of curation is markedly higher for both Gunnarsrød 6b and Vallermyrene 4 when the non-flint material is excluded, although they remain more expedient than that of contemporary assemblages. Thus, the degree of expediency for assemblages dominated by non-flint materials might be somewhat exaggerated when the non-flint material is included, while its exlusion would underplay. As the measure and general tendencies appear relatively robust to this effect, this it not explored further here, however, one possible approach could be to weight the curation index by proportion of non-flint material in the assemblages. Another case also worth commenting on is Langangen Vestgård 1, which, on the grounds of a overall large number of artefacts and the possible presence of a dwelling structure was argued to reflect a more permanent site location in the original report (Melvold and Eigeland, 2014). However, the relatively high value on the curation index could mean that Langangen Vestgård 1 reflects the aggregation of stays which predominantly have been of a relatively shorter duration, while the possible dwelling structure, if taken as an indication of longer stays, could in this perspective represent a remnant from one or a few visits of longer duration that constitute a smaller fraction of the use-life of the site as a whole.

While there are certainly nuances and idiosyncrasies in the material that might lead one to question the applicability of the VDL and RFSL measures for any individual site, the overall pattern for curation does appear relatively robust. It seems clear that there is a marked drop in curation from just before 8000 BCE, which stabilises around 7500 BCE. This would correspond well with chronological where the end of the Early Mesolithic, or flake axe phase, is set to last until c. 8200 BCE. The following phase linked to the production of microliths is in this framework set to last to around 7500 BCE, signifying the start of the production of the non-flint ground-pecked or round butted adzes. Later chronological transitions suggested at c. 5600 and c. 4500 BCE, are not reflected in fluctuations in the curation index. While this can be traced in the CA with the reduced importance of the non-flint categories, this could indicate that these changes are not related mobility patterns.

the exlusion of this material The patterns evident in the flint data could thus indicate that only the period from around 8000 to 7500 BCE has individual sites that have had more stable roles within a settlement system, where some predominantly have functioned as base-camps. The lower degree of variation in the proceeding and preceeding phases could reflect either a more uniform purpose of visits to the sites, or that each assemblage consitutes the aggregation of visits of different purposes that in effect cancel out. Given the overall higher expediency in the earliest part of the Mesolithic this could be in line with a residental mobility pattern, while the high expediency on some sites when considering all lithics in aggregate in the later parts of the Mesolithic does speak to variability in site use. The nature of the non-flint material does leave the signficance of this pattern more difficult to directly align with the residential-logistic continuum.

It is also interesting to note that some of the chronological patterns indicated in the CA does not appear to be related to the curation measures. This for example pertains to the apparent importance of the axe production sites. It has recently been suggested that the end of the Mesolithic sees a drop in population numbers and a subsequent increase in mobility as migrating populations arrived from southern Scandinavia. It is an open question whether the apparent absence of this pattern in the present data is a result of a strained sample size, or a weaker or non-existent signal from any changes in mobility patterns in the later Mesolithic. As it stands, however, the main hypothesis generated by the present data would be that settlement patterns in the earliest parts of the Mesolithic were characterised by relatively high degrees of mobility, which then drop before leveling off at around 7000 BCE and remain relatively stable throughout the rest of the period. Even though the relative increase in Given the parallel increase in variation this would seem to correlate well with a transition from a residential to a logistical settlement system.

As for the measures associated with curation and mobility patterns, relative frequency of secondarily worked lithics and volumentric density of lithics correspond most clearly to patterns previously proposed in the literature. However, as these were identified by employing PCA and identifying what variables most clearly correspond to a temporal dimension, there is a danger of circular reasoning. Having established that these measures appears to capture a behavioural dimension of interest, this can, however, now be tested more rigorously by employing site data from a wider region. these results do speak to the cross-cultural and cross-temporal applicability of the measure, and consequently the potential it holds for comparative studies. The findings indicated by the WABI, as well as by the negative correlation between relative frequency of primarily and secondarily worked lithics, do align more clearly with previous suggestions concerning the overall mobility patterns in Mesolithic Norway. However, the question then becomes: What is the significance of the negatively correlated variables of relative frequency of cores and chips? Given that these are more or less orthogonal to the WABI variables on the first and second component, this would indicate that if one of these variable pairs is taken to reflect the scale between expedient and curated technologies, it should follow that the other variable pair is not correlated with these. As the entire temporal range of the sites is spread out between the extremes of the chip and core variables, one possibility is that these are indicative of site types that are temporally ubiquitous.

This is arguably more established than the variables included bysimilarities between the variable plots of Bicho and Cascalheira (2020) and the variables indicated in is striking. This pertains to the negative correlation between core and chip frequency, and the negative correlation between primary and secondarily worked lithics. This last

These measures are taken from the framework of whole assemblage behavioural index, as developed by , and is aimed to get at behavioural dimensions relating to mobility patterns among hunter-gatherers.

4 Conclusion

The results of the CA using the entirety of the lithic assemblages align more or less with results of previous research in south-eastern Norway. This would indicate that in general, meaningful chronological patterning is associated with the employed artefact categories. These tendencies are already well-established when it comes to the formal tool types, but have been given less focus in light of entire assemblages. these notions have previously Precisely what behavioural implication the development in the use of these types have, however, is unclear, but appears to follow a different and more complex trajectory than that of curation, as measured here.

In my view this alignes well with the framework of Clark and Barton (2017), suggesting that shifts in mobility patterns has caused this empirical pattern. However, the role of non-flint material, variability of flint quality. Furthermore, precisely how this mobility pattern has. Here Binfords model of briefly invoked, but there exists of course a wide range of possible configurations and mobility strategies that have subsistence data, site locations

The results of considering variables that are have been associated with mobility patterns gave rise to two competing hypotheses. These pertain to whether the degree of curation associated with the assemblages as captured by the changing ratio of primary to secondarily worked lithics over time is the result of changing raw material availability or mobility patterns. The orthogonal relationship between core to chip frequency would in this framework be expected to be capturing the opposite relationship. If degree of curation mainly reflects changes in raw-material quality and availability, this would then indicate that mobility patterns are comparable throughout the Mesolithic as the core to chip frequency is not time-dependent. And vice verca — if the changing mobility patterns is the main driver of the transition from curated to expedient technological organisation, as operationalised here, While this would be in line with findings from a recent evaluation of the locational patterns associated with the sites (Roalkvam, 2020), there is no readily available empirical evidence, no smoking gun, se findings are the results of an exploratory analysis, and are therefore perhaps best seen as potentially laying the framework for later targeted strategic test of these competing hypotheses.

5 References

Andrefsky, W., 1994. Raw-material availability and the organization of technology. American Antiquity 59, 21–34. https://doi.org/10.2307/3085499

Åstveit, L.I., 2018. The early mesolithic of western norway, in: Blankholm, H.P. (Ed.),. Equinox, Sheffield, pp. 231–274.

Bailey, G., 2007. Time perspectives, palimpsests and the archaeology of time. Journal of Anthropological Archaeology 26, 198–223. https://doi.org/10.1016/j.jaa.2006.08.002

Bamforth, D.B., 1986. Technological Efficiency and Tool Curation. American Antiquity 51, 38–50. https://doi.org/10.2307/280392

Bang-Andersen, S., 2003. Southwest Norway at the Pleistocene/Holocene Transition: Landscape Development, Colonization, Site Types, Settlement Patterns. Norwegian Archaeological Review 36, 5–25. https://doi.org/10.1080/00293650307293

Barton, C.M., 1991. Retouched Tools, Fact or Fiction? Paradigms for Interpreting Paleolithic Chipped Stone, in: Clark, G.A. (Ed.),. University of Pennsylvania Press, pp. 143–163.

Barton, C.M., Bernabeu, J., Aura, J.E., García, O., 1999. Land-Use Dynamics and Socioeconomic Change: An Example from the Polop Alto Valley. American Antiquity 64, 609–634. https://doi.org/10.2307/2694208

Barton, C.M., Riel-Salvatore, J., 2014. The formation of lithic assemblages. Journal of Archaeological Science 46, 334–352. https://doi.org/10.1016/j.jas.2014.03.031

Barton, C.M., Riel-Salvatore, J., Anderies, J.M., Popescu, G., 2011. Modeling Human Ecodynamics and Biocultural Interactions in the Late Pleistocene of Western Eurasia. Human Ecology 39, 705–725. https://doi.org/10.1007/s10745-011-9433-8

Baxter, M.J., 1994. Exploratory multivariate analysis in archaeology. Percheron Press, New York.

Baxter, M.J., Cool, H.E.M., 2016. Reinventing the wheel? Modelling temporal uncertainty with applications to brooch distributions in Roman Britain. Journal of Archaeological Science 66, 120–127. https://doi.org/10.1016/j.jas.2015.12.007

Berg-Hansen, I.M., 2017. Den sosiale teknologien. Teknologi og tradisjon i nord-europa ved slutten av istida, 10 900 - 8500 f.kr. (PhD thesis). Oslo.

Berg-Hansen, I.M., 1999. The availability of flint at lista and jæren, southwestern norway, in: Boaz, J. (Ed.),. University of Oslo, Oslo, pp. 255–266.

Bevan, A., 2015. The data deluge. Antiquity 89, 1473–1484. https://doi.org/10.15184/aqy.2015.102

Bicho, N., Cascalheira, J., 2020. Use of lithic assemblages for the definition of short-term occupations in hunter-gatherer prehistory, in: Cascalheira, J., Picin, A. (Eds.),. Springer, Cham, p. 1938. https://doi.org/10.1007/978-3-030-27403-0₂

Binford, L.R., 1979. Organization and Formation Processes: Looking at Curated Technologies. Journal of Anthropological Research 35, 255–273.

Binford, L.R., 1973. Interassemblage variability - the mousterian and the "functional" argument, in: Renfrew, C. (Ed.),. Duckworth, London, pp. 227–254.

Bjerck, H.B., 2010. Norwegian mesolithic trends: A review. Cambridge University Press, Cambridge, pp. 60–106.

Breivik, H.M., 2020. Diachronic trends among early mesolithic sites types? A study from the coast of central norway, in: Schülke, A. (Ed.),. Rutledge, London & New York, pp. 121–146.

Breivik, H.M., Callanan, M., 2016. Hunting high and low: Postglacial colonization strategies in central norway between 9500 and 8000 cal bc. European Journal of Archaeology 19, 571–595. https://doi.org/10.1080/14619571.2016.1147315

Breivik, H.M., Fossum, G., Solheim, S., 2018. Exploring human responses to climatic fluctuations and environmental diversity: Two stories from Mesolithic Norway. Quaternary International, Impacts of gradual and abrupt environmental changes on Late glacial to Middle Holocene cultural changes in Europe 465, 258–275. https://doi.org/10.1016/j.quaint.2016.12.019

Bronk Ramsey, C., 2009. Bayesian Analysis of Radiocarbon Dates. Radiocarbon 51, 337–360. https://doi.org/10.1017/S0033822200033865

Carrasco, L., Eggen, I.M., Eigeland, L., Fossum, G., Melvold, S., Persson, P., Reitan, G., 2014. Gunnarsrød 6. Et boplassområde fra overgangen mellommesolitikum-seinmesolitikum, in: Melvold, S., Persson, P. (Eds.),. Portal forlag, Kristiansand, pp. 277–308.

Clark, G.A., 2009. Accidents of history: Conceptual frameworks in paleoarchaeology, in: Camps, M., Chauhan, P. (Eds.),. Springer, New York, pp. 19–41.

Clark, G.A., Barton, C.M., 2017. Lithics, landscapes & la Longue-durée Curation & expediency as expressions of forager mobility. Quaternary International, Prehistoric hunter-gatherers and farmers in the Adriatic and neighboring regions 450, 137–149. https://doi.org/10.1016/j.quaint.2016.08.002

Clark, G.A., Riel-Salvatore, J., 2006. Observations on systematics in paleolithic archaeology, in: Hovers, E., Kuhn, S. (Eds.),. Springer, New York, pp. 29–56.

Crema, E.R., 2012. Modelling Temporal Uncertainty in Archaeological Analysis. Journal of Archaeological Method and Theory 19, 440–461. https://doi.org/10.1007/s10816-011-9122-3

Damlien, H., 2016. Eastern pioneers in westernmost territories? Current perspectives on Mesolithic huntergatherer large-scale interaction and migration within Northern Eurasia. Quaternary International, Holocene Hunter-Gatherer Archaeology of Northern Eurasia (Guest Editors: Peter Jordan and Andrzej Weber) 419, 5–16. https://doi.org/10.1016/j.quaint.2014.02.023

Damlien, H., Solheim, S., 2018. The pioneer settlement of eastern norway, in: Blankholm, H.P. (Ed.),. Equinox, Sheffield, pp. 335–367.

Dibble, H.L., 1995. Middle paleolithic scraper reduction: Background, clarification, and review of the evidence to date. Journal of Archaeological Method and Theory 2, 299–368. https://doi.org/10.1007/BF02229003

Dibble, H.L., Holdaway, S.J., Lin, S.C., Braun, D.R., Douglass, M.J., Iovita, R., McPherron, S.P., Olszewski, D.I., Sandgathe, D., 2017. Major Fallacies Surrounding Stone Artifacts and Assemblages. Journal of Archaeological Method and Theory 24, 813–851. https://doi.org/10.1007/s10816-016-9297-8

Eigeland, L., 2015. Maskinmennesket i steinalderen. Endring og kontinuitet i steinteknologi fram mot neolitiseringen av øst-norge (PhD thesis). Oslo.

Eigeland, L., Fossum, G., 2017. Hegna vest 4. En mellommesolittisk lokalitet med to funnkonsentrasjoner, in: Solheim, S. (Ed.),. Portal forlag, Kristiansand, pp. 357–370.

Frivoll, A., 2017. Identifisering og klassifisering av littiske råmaterialer i sør- og østnorsk steinalderforskning. Reliabilitet av visuell klassifiseringsmetode (PhD thesis). Oslo.

Glørstad, H., 2011. The Nøstvet axe, in: Davis, V., Edmonds, M. (Eds.),. Oxbow Books, pp. 21–36.

Helskog, K., Indrelid, S., Mikkelsen, E., 1976. Morfologisk klassifisering av slåtte steinartefakter. Universitetets Oldsaksamling Årbok 1972-74, 9–40.

Hinz, M., Schmid, C., Knitter, D., Tietze, C., 2021. oxcAAR: Interface to ’OxCal’ radiocarbon calibration. R package version 1.1.0.

Jaksland, L., 2014. Kulturhistorisk sammenstilling, in: Jaksland, L., Persson, P. (Eds.),. University of Oslo, Museum of Cultural History, Oslo, pp. 11–46.

Jaksland, L., Fossum, G., 2014. Kronologiske trender i det littiske funnmaterialet. Typologi, teknologi og råstoff, in: Jaksland, L., Persson, P. (Eds.),. University of Oslo, Museum of Cultural History, Oslo, pp. 47–62.

Jaksland, L., Persson, P. (Eds.), 2014. E18 brunlanesprosjektet. Bind i. Forutsetninger og kulturhistorisk sammenstilling. University of Oslo, Museum of Cultural History, Oslo.

Jelinek, A.J., 1976. Form, function and style in lithic analysis, in: Cleland, C.E. (Ed.),. Academic Press, New York, pp. 19–33.

Jørgensen, E.K., 2017. Om vegetasjonsforstyrrelser: Konsekvenser for bevaringen av arkeologisk kontekstinformasjon i norske jordsmonn. Viking 80, 157180–157180. https://doi.org/10.5617/viking.5477

Kitchel, N., Aldenderfer, M.S., Haas, R., 2021. Diet, Mobility, Technology, and Lithics: Neolithization on the Andean Altiplano, 7.03.5 ka. Journal of Archaeological Method and Theory. https://doi.org/10.1007/s10816-021-09525-7

Kuhn, S.L., 1992. On Planning and Curated Technologies in the Middle Paleolithic. Journal of Anthropological Research 48, 185–214.

Lockyear, K., 2000. Site Finds in Roman Britain: A Comparison of Techniques. Oxford Journal of Archaeology 19, 397–423. https://doi.org/https://doi.org/10.1111/1468-0092.00118

Manninen, M.A., Knutsson, K., 2014. Lithic raw material diversification as an adaptive strategyTechnology, mobility, and site structure in Late Mesolithic northernmost Europe. Journal of Anthropological Archaeology 33, 84–98. https://doi.org/10.1016/j.jaa.2013.12.001

Mansrud, A., Eymundsson, C., 2016. Socialized landscapes? Lithic clusters, hearths and relocation rituals at Middle Mesolithic sites in Eastern Norway. Fennoscandia archaeologica 33, 27–55.

Marwick, B., 2017. Computational reproducibility in archaeological research: Basic principles and a case study of their implementation. Journal of Archaeological Method and Theory 24, 424–450. https://doi.org/10.1007/s10816-015-9272-9

Marwick, B., Boettiger, C., Mullen, L., 2018. Packaging data analytical work reproducibly using r (and friends). The American Statistician 72, 80–88. https://doi.org/10.1007/s10816-015-9272-9

Melvold, S., Eigeland, L., 2014. Langangen vestgård 1. En boplass fra siste del av mellommesolitikum med trinnøksproduksjon og strukturer, in: Melvold, S., Persson, P. (Eds.),. Portal forlag, Kristiansand, pp. 239–276.

Melvold, S., Persson, P. (Eds.), 2014. Vestfoldbaneprosjektet. Arkeologiske undersøkelser i forbindelse med ny jernbane mellom larvik og porsgrunn. Bind 1. Tidlig- og mellommesolittiske lokaliteter i vestfold og telemark. Portal forlag, Kristiansand.

Orton, D., Morris, J., Pipe, A., 2017. Catch Per Unit Research Effort: Sampling Intensity, Chronological Uncertainty, and the Onset of Marine Fish Consumption in Historic London. Open Quaternary 3, 1. https://doi.org/10.5334/oq.29

Parry, W.J., Kelly, R.L., 1987. Expedient core technology and sedentism, in: Johnson, J.K., Morrow, C.A. (Eds.),. Westview Press, Boulder & London, pp. 285–308.

Perreault, C., 2019. The quality of the archaeological record. The University of Chicago Press, Chicago & London.

Persson, P., 2014. Prestemoen 1. En plats med ben från mellanmesolitikum, in: Melvold, S., Persson, P. (Eds.),. Portal forlag, Kristiansand, pp. 202–227.

R Core Team, 2020. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna.

Reitan, G., 2016. Mesolittisk kronologi i Sørøst-Norge et forslag til justering. Viking 79, 23–51. https://doi.org/10.5617/viking.3903

Reitan, G., Persson, P. (Eds.), 2014. Vestfoldbaneprosjektet. Arkeologiske undersøkelser i forbindelse med ny jernbane mellom larvik og porsgrunn. Bind 2. Seinmesolittiske, neolittiske og yngre lokaliteter i vestfold og telemark. Portal forlag, Kristiansand.

Rezek, Z., Holdaway, S.J., Olszewski, D.I., Lin, S.C., Douglass, M., McPherron, S.P., Iovita, R., Braun, D.R., Sandgathe, D., 2020. Aggregates, Formational Emergence, and the Focus on Practice in Stone Artifact Archaeology. Journal of Archaeological Method and Theory 27, 887–928. https://doi.org/10.1007/s10816-020-09445-y

Roalkvam, I., 2020. Algorithmic Classification and Statistical Modelling of Coastal Settlement Patterns in Mesolithic South-Eastern Norway. Journal of Computer Applications in Archaeology 3, 288307. https://doi.org/10.5334/jcaa.60

Romundset, A., Lakeman, T.R., Høgaas, F., 2018. Quantifying variable rates of postglacial relative sea level fall from a cluster of 24 isolation basins in southern Norway. Quaternary Science Reviews 197, 175–192. https://doi.org/10.1016/j.quascirev.2018.07.041

Shott, M.J., 1996. An Exegesis of the Curation Concept. Journal of Anthropological Research 52, 259–280. https://doi.org/10.1086/jar.52.3.3630085

Shott, M.J., Sillitoe, P., 2005. Use life and curation in New Guinea experimental used flakes. Journal of Archaeological Science 32, 653–663. https://doi.org/10.1016/j.jas.2004.11.012

Smith, G.M., 2015. Modeling the influences of raw material availability and functional efficiency on obsidian projectile point curation: A Great Basin example. Journal of Archaeological Science: Reports 3, 112–121. https://doi.org/10.1016/j.jasrep.2015.06.010

Solheim, S. (Ed.), 2017. E18 rugtvedt-dørdal. Arkeologiske undersøkelser av lokaliteter fra steinalder og jernalder i bamble kommune, telemark fylke. Portal forlag, Kristiansand.

Solheim, S., Damlien, H. (Eds.), 2013. E18 bommestad-sky. Undersøkelse av lokaliteter fra mellommesolitikum, larvik kommune, vestfold fylke. Portal forlag, Kristiansand.

Solheim, S., Damlien, H., Fossum, G., 2020. Technological transitions and human-environment interactions in Mesolithic southeastern Norway, 11 5006000 cal. BP. Quaternary Science Reviews 246, 106501. https://doi.org/10.1016/j.quascirev.2020.106501

Solheim, S., Fossum, G., Knutsson, H., 2018. Use-wear analysis of Early Mesolithic flake axes from South-eastern Norway. Journal of Archaeological Science: Reports 17, 560–570. https://doi.org/10.1016/j.jasrep.2017.12.017

Solheim, S., Olsen, D.E.F., 2013. Hovland 3. Mellommesolittisk boplass med hyttetuft, in: Solheim, S., Damlien, H. (Eds.),. Portal forlag, Kristiansand, pp. 198–235.

Surovell, T.A., 2009. Toward a behavioral ecology of lithic technology. The University of Arizona Press, Tucson.

Sørensen, R., 1979. Late Weichselian deglaciation in the Oslofjord area, south Norway. Boreas 8, 241–246. https://doi.org/https://doi.org/10.1111/j.1502-3885.1979.tb00806.x

Sørensen, R., Henningsmoen, K.E., Høeg, H.I., Gälman, V., 2014. Holocene landhevningsstudier i søndre vestfold og sørøstre telemark- revidert kurve, in: Melvold, S., Persson, P. (Eds.),. Portal, Kristiansand, pp. 36–47.

Viken, S., 2018. Sagene B1. En tidligmesolittisk basisboplass med én boligstruktur og spor etter flere samtidige hushold, in: Reitan, G., Sundström, L. (Eds.),. Cappelen Damm Akademisk, Oslo, pp. 131–166.

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#> [1] /home/isak/R/x86_64-pc-linux-gnu-library/4.0
#> [2] /usr/local/lib/R/site-library
#> [3] /usr/lib/R/site-library
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The current Git commit details are:

#> Local:    master /home/isak/phd/dialpast_r/dialpastrepository
#> Remote:   master @ origin (https://github.com/isakro/dialpastrepository.git)
#> Head:     [0a2c418] 2021-05-22: Fixed temporal plot after flipping dates